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Identifying heterogeneous health profiles of primary care utilizers and their differential healthcare utilization and mortality - a retrospective cohort study.

ABSTRACT: BACKGROUND:Heterogeneity of population health needs and the resultant difficulty in health care resources planning are challenges faced by primary care systems globally. To address this challenge in population health management, it is critical to have a better understanding of primary care utilizers' heterogeneous health profiles. We aimed to segment a population of primary care utilizers into classes with unique disease patterns, and to report the 1?year follow up healthcare utilizations and all-cause mortality across the classes. METHODS:Using de-identified administrative data, we included all adult Singapore citizens or permanent residents who utilized Singapore Health Services (SingHealth) primary care services in 2012. Latent class analysis was used to identify patient subgroups having unique disease patterns in the population. The models were assessed by Bayesian Information Criterion and clinical interpretability. We compared healthcare utilizations in 2013 and one-year all-cause mortality across classes and performed regression analysis to assess predictive ability of class membership on healthcare utilizations and mortality. RESULTS:We included 100,747 patients in total. The best model (k?=?6) revealed the following classes of patients: Class 1 "Relatively healthy" (n?=?58,213), Class 2 "Stable metabolic disease" (n?=?26,309), Class 3 "Metabolic disease with vascular complications" (n?=?2964), Class 4 "High respiratory disease burden" (n?=?1104), Class 5 "High metabolic disease without complication" (n?=?11,122), and Class 6 "Metabolic disease with multi-organ complication" (n?=?1035). The six derived classes had different disease patterns in 2012 and 1 year follow up healthcare utilizations and mortality in 2013. "Metabolic disease with multiple organ complications" class had the highest healthcare utilization (e.g. incidence rate ratio?=?19.68 for hospital admissions) and highest one-year all-cause mortality (hazard ratio?=?27.97). CONCLUSIONS:Primary care utilizers are heterogeneous and can be segmented by latent class analysis into classes with unique disease patterns, healthcare utilizations and all-cause mortality. This information is critical to population level health resource planning and population health policy formulation.

SUBMITTER: Yan S

PROVIDER: S-EPMC6477732 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Identifying heterogeneous health profiles of primary care utilizers and their differential healthcare utilization and mortality - a retrospective cohort study.

Yan Shi S Seng Benjamin Jun Jie BJJ Kwan Yu Heng YH Tan Chuen Seng CS Quah Joanne Hui Min JHM Thumboo Julian J Low Lian Leng LL

BMC family practice 20190423 1

<h4>Background</h4>Heterogeneity of population health needs and the resultant difficulty in health care resources planning are challenges faced by primary care systems globally. To address this challenge in population health management, it is critical to have a better understanding of primary care utilizers' heterogeneous health profiles. We aimed to segment a population of primary care utilizers into classes with unique disease patterns, and to report the 1 year follow up healthcare utilization ...[more]

PMID: 31014231

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Project description:BACKGROUND:High-risk patients are most vulnerable during transitions of care. Due to the high burden of resource allocation for such patients, we propose that segmentation of this heterogeneous population into distinct subgroups will enable improved healthcare resource planning. In this study, we segmented a high-risk population with the aim to identify and characterize a patient subgroup with the highest 30-day and 90-day hospital readmission and mortality. METHODS:We extracted data from our transitional care program (TCP), a Hospital-to-Home program launched by the Singapore Ministry of Health, from June to November 2018. Latent class analysis (LCA) was used to determine the optimal number and characteristics of latent subgroups, assessed based on model fit and clinical interpretability. Regression analysis was performed to assess the association of class membership on 30- and 90-day all-cause readmission and mortality. RESULTS:Among 752 patients, a 3-class best fit model was selected: Class 1 "Frail, cognitively impaired and physically dependent", Class 2 "Pre-frail, but largely physically independent" and Class 3 "Physically independent". The 3 classes have distinct demographics, medical and socioeconomic characteristics (p < 0.05), 30- and 90-day readmission (p < 0.05) and mortality (p < 0.01). Class 1 patients have the highest age-adjusted 90-day readmission (OR = 2.04, 95%CI: 1.21-3.46, p = 0.008), 30- (OR = 6.92, 95%CI: 1.76-27.21, p = 0.006) and 90-day mortality (OR = 11.51, 95%CI: 4.57-29.02, p < 0.001). CONCLUSIONS:We identified a subgroup with the highest readmission and mortality risk amongst high-risk patients. We also found a lack of interventions in our TCP that specifically addresses increased frailty and poor cognition, which are prominent features in this subgroup. These findings will help to inform future program modifications and strengthen existing transitional healthcare structures currently utilized in this patient cohort.

Project description:Background Canadian Cardiovascular Society (CCS) angina severity classification is associated with mortality, myocardial infarction, and coronary revascularization in clinical trial and registry data. The objective of this study was to determine associations between CCS class and all-cause mortality and healthcare utilization, using natural language processing to extract CCS classifications from clinical notes. Methods and Results In this retrospective cohort study of veterans in the United States with stable angina from January 1, 2006, to December 31, 2013, natural language processing extracted CCS classifications. Veterans with a prior diagnosis of coronary artery disease were excluded. Outcomes included all-cause mortality (primary), all-cause and cardiovascular-specific hospitalizations, coronary revascularization, and 1-year healthcare costs. Of 299 577 veterans identified, 14 216 (4.7%) had ≥1 CCS classification extracted by natural language processing. The mean age was 66.6±9.8 years, 99% of participants were male, and 81% were white. During a median follow-up of 3.4 years, all-cause mortality rates were 4.58, 4.60, 6.22, and 6.83 per 100 person-years for CCS classes I, II, III, and IV, respectively. Multivariable adjusted hazard ratios for all-cause mortality comparing CCS II, III, and IV with those in class I were 1.05 (95% CI, 0.95-1.15), 1.33 (95% CI, 1.20-1.47), and 1.48 (95% CI, 1.25-1.76), respectively. The multivariable hazard ratio comparing CCS IV with CCS I was 1.20 (95% CI, 1.09-1.33) for all-cause hospitalization, 1.25 (95% CI, 0.96-1.64) for acute coronary syndrome hospitalizations, 1.00 (95% CI, 0.80-1.26) for heart failure hospitalizations, 1.05 (95% CI, 0.88-1.25) for atrial fibrillation hospitalizations, 1.92 (95% CI, 1.40-2.64) for percutaneous coronary intervention, and 2.51 (95% CI, 1.99-3.16) for coronary artery bypass grafting surgery. Conclusions Natural language processing-extracted CCS classification was positively associated with all-cause mortality and healthcare utilization, demonstrating the prognostic importance of anginal symptom assessment and documentation.

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Identifying heterogeneous health profiles of primary care utilizers and their differential healthcare utilization and mortality - a retrospective cohort study.

Publications

Identifying heterogeneous health profiles of primary care utilizers and their differential healthcare utilization and mortality - a retrospective cohort study.

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